Hamiltonian Systems and Transformation in Hilbert Space
PulseAugur coverage of Hamiltonian Systems and Transformation in Hilbert Space — every cluster mentioning Hamiltonian Systems and Transformation in Hilbert Space across labs, papers, and developer communities, ranked by signal.
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New framework formalizes structure encoding in AI representations
Researchers have introduced a new framework called Legendre dynamics, which formalizes how internal representations in learning systems can encode underlying physical or statistical structure. This approach uses Legendr…
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New neural network framework learns complex Lie-Poisson system dynamics
Researchers have developed Latent Lie-Poisson Neural Networks (LLPNNs), a novel framework designed to learn and predict the dynamics of Lie-Poisson systems directly from observable data. These systems are crucial for mo…
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New ML frameworks learn complex physics dynamics with enhanced prediction
Two new research papers introduce advanced machine learning frameworks for predicting the dynamics of complex physical systems. The first, CaLiSym, extends exact symplectic learning to systems with energy exchange with …
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New autoencoder preserves symplectic structure in model reduction
Researchers have developed a new method for reducing the dimensionality of complex Hamiltonian systems while preserving their essential symplectic structure. This approach, called symplecticity-preserving autoencoders (…